Overview

Dataset statistics

Number of variables17
Number of observations71102
Missing cells26294
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 MiB
Average record size in memory136.0 B

Variable types

NUM11
CAT6

Warnings

anio has constant value "71102" Constant
colonia has a high cardinality: 1340 distinct values High cardinality
consumo_prom_no_dom is highly correlated with consumo_promHigh correlation
consumo_prom is highly correlated with consumo_prom_no_domHigh correlation
alcaldia is highly correlated with nomgeoHigh correlation
nomgeo is highly correlated with alcaldiaHigh correlation
consumo_total_mixto has 8327 (11.7%) missing values Missing
consumo_prom_dom has 4820 (6.8%) missing values Missing
consumo_total_dom has 4820 (6.8%) missing values Missing
consumo_prom_mixto has 8327 (11.7%) missing values Missing
consumo_total_mixto is highly skewed (γ1 = 21.76535468) Skewed
consumo_prom_dom is highly skewed (γ1 = 74.81862948) Skewed
consumo_prom_mixto is highly skewed (γ1 = 43.60044406) Skewed
consumo_prom is highly skewed (γ1 = 43.38268186) Skewed
consumo_prom_no_dom is highly skewed (γ1 = 40.71654298) Skewed
consumo_total_no_dom is highly skewed (γ1 = 22.5073679) Skewed
gid has unique values Unique
consumo_total_mixto has 17715 (24.9%) zeros Zeros
consumo_prom_dom has 9861 (13.9%) zeros Zeros
consumo_total_dom has 9861 (13.9%) zeros Zeros
consumo_prom_mixto has 17715 (24.9%) zeros Zeros
consumo_total has 2451 (3.4%) zeros Zeros
consumo_prom has 2451 (3.4%) zeros Zeros
consumo_prom_no_dom has 8109 (11.4%) zeros Zeros
consumo_total_no_dom has 8109 (11.4%) zeros Zeros

Reproduction

Analysis started2020-09-30 03:26:25.165436
Analysis finished2020-09-30 03:26:59.811365
Duration34.65 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

consumo_total_mixto
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct24339
Distinct (%)38.8%
Missing8327
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean174.3599291
Minimum0
Maximum23404.44
Zeros17715
Zeros (%)24.9%
Memory size555.5 KiB
2020-09-29T22:27:00.041452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median79.94
Q3233.32
95-th percentile660.779
Maximum23404.44
Range23404.44
Interquartile range (IQR)233.32

Descriptive statistics

Standard deviation312.663596
Coefficient of variation (CV)1.793207864
Kurtosis1419.360189
Mean174.3599291
Median Absolute Deviation (MAD)79.94
Skewness21.76535468
Sum10945444.55
Variance97758.52424
MonotocityNot monotonic
2020-09-29T22:27:00.245745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01771524.9%
 
36740.1%
 
17.7610.1%
 
36.6590.1%
 
18.3540.1%
 
29.28520.1%
 
57.96500.1%
 
23.8480.1%
 
43.32470.1%
 
46.98460.1%
 
Other values (24329)4456962.7%
 
(Missing)832711.7%
 
ValueCountFrequency (%) 
01771524.9%
 
0.121< 0.1%
 
0.244< 0.1%
 
0.273< 0.1%
 
0.354< 0.1%
 
ValueCountFrequency (%) 
23404.441< 0.1%
 
23058.91< 0.1%
 
23031.061< 0.1%
 
5979.711< 0.1%
 
5974.321< 0.1%
 

anio
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
2019
71102 
ValueCountFrequency (%) 
201971102100.0%
 
2020-09-29T22:27:00.613859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-29T22:27:00.713693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:27:00.835233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

nomgeo
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
Iztapalapa
10515 
Gustavo A. Madero
10058 
Cuauhtémoc
7313 
Benito Juárez
6049 
Venustiano Carranza
5179 
Other values (12)
31988 
ValueCountFrequency (%) 
Iztapalapa1051514.8%
 
Gustavo A. Madero1005814.1%
 
Cuauhtémoc731310.3%
 
Benito Juárez60498.5%
 
Venustiano Carranza51797.3%
 
Miguel Hidalgo51107.2%
 
Coyoacán49477.0%
 
Azcapotzalco42165.9%
 
Álvaro Obregón41405.8%
 
Iztacalco34694.9%
 
Other values (7)1010614.2%
 
2020-09-29T22:27:01.099518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-29T22:27:01.354017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length12
Mean length12.40342044
Min length6

consumo_prom_dom
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct52060
Distinct (%)78.5%
Missing4820
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean29.13238577
Minimum0
Maximum7796.41
Zeros9861
Zeros (%)13.9%
Memory size555.5 KiB
2020-09-29T22:27:01.597143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.69054691
median26.41424809
Q336.24656251
95-th percentile59.39294171
Maximum7796.41
Range7796.41
Interquartile range (IQR)17.5560156

Descriptive statistics

Standard deviation64.56592495
Coefficient of variation (CV)2.216293765
Kurtosis7663.654738
Mean29.13238577
Median Absolute Deviation (MAD)8.738705357
Skewness74.81862948
Sum1930952.794
Variance4168.758665
MonotocityNot monotonic
2020-09-29T22:27:01.804323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0986113.9%
 
1.2233< 0.1%
 
14.6423< 0.1%
 
10.9822< 0.1%
 
15.2522< 0.1%
 
9.1521< 0.1%
 
9.7621< 0.1%
 
11.5920< 0.1%
 
20.4820< 0.1%
 
7.9320< 0.1%
 
Other values (52050)5621979.1%
 
(Missing)48206.8%
 
ValueCountFrequency (%) 
0986113.9%
 
0.0099999997761< 0.1%
 
0.021< 0.1%
 
0.122< 0.1%
 
0.12999999521< 0.1%
 
ValueCountFrequency (%) 
7796.411< 0.1%
 
7581.691< 0.1%
 
6073.4599611< 0.1%
 
3726.51< 0.1%
 
3622.21< 0.1%
 

consumo_total_dom
Real number (ℝ≥0)

MISSING
ZEROS

Distinct47051
Distinct (%)71.0%
Missing4820
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean1186.263611
Minimum0
Maximum95060.69
Zeros9861
Zeros (%)13.9%
Memory size555.5 KiB
2020-09-29T22:27:02.123642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1161.635
median604.185
Q31261.445
95-th percentile4027.52
Maximum95060.69
Range95060.69
Interquartile range (IQR)1099.81

Descriptive statistics

Standard deviation2771.038307
Coefficient of variation (CV)2.33593805
Kurtosis248.0413047
Mean1186.263611
Median Absolute Deviation (MAD)517.3
Skewness12.52320362
Sum78627924.68
Variance7678653.301
MonotocityNot monotonic
2020-09-29T22:27:02.431223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0986113.9%
 
1.22370.1%
 
10.9821< 0.1%
 
25.6220< 0.1%
 
3.6620< 0.1%
 
14.6420< 0.1%
 
15.2519< 0.1%
 
18.319< 0.1%
 
7.9319< 0.1%
 
17.6918< 0.1%
 
Other values (47041)5622879.1%
 
(Missing)48206.8%
 
ValueCountFrequency (%) 
0986113.9%
 
0.121< 0.1%
 
0.241< 0.1%
 
0.52< 0.1%
 
0.61< 0.1%
 
ValueCountFrequency (%) 
95060.691< 0.1%
 
94021.71< 0.1%
 
90078.441< 0.1%
 
83309.941< 0.1%
 
82689.381< 0.1%
 

alcaldia
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
iztapalapa
10515 
gustavo a. madero
10058 
cuauhtemoc
7313 
benito juarez
6049 
venustiano carranza
5179 
Other values (11)
31988 
ValueCountFrequency (%) 
iztapalapa1051514.8%
 
gustavo a. madero1005814.1%
 
cuauhtemoc731310.3%
 
benito juarez60498.5%
 
venustiano carranza51797.3%
 
miguel hidalgo51107.2%
 
coyoacan49477.0%
 
azcapotzalco42165.9%
 
alvaro obregon41405.8%
 
iztacalco34694.9%
 
Other values (6)1010614.2%
 
2020-09-29T22:27:02.691085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-29T22:27:03.034279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length12
Mean length12.25522489
Min length7

colonia
Categorical

HIGH CARDINALITY

Distinct1340
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
centro
 
1139
agricola oriental
 
837
roma norte
 
602
moctezuma 2a seccion
 
558
jardin balbuena
 
498
Other values (1335)
67468 
ValueCountFrequency (%) 
centro11391.6%
 
agricola oriental8371.2%
 
roma norte6020.8%
 
moctezuma 2a seccion5580.8%
 
jardin balbuena4980.7%
 
doctores4900.7%
 
san felipe de jesus4190.6%
 
obrera4180.6%
 
roma sur4180.6%
 
agricola pantitlan4170.6%
 
Other values (1330)6530691.8%
 
2020-09-29T22:27:03.293365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-29T22:27:03.551535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length43
Median length16
Mean length16.86555934
Min length4

consumo_prom_mixto
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct31911
Distinct (%)50.8%
Missing8327
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean50.63623377
Minimum0
Maximum11702.22
Zeros17715
Zeros (%)24.9%
Memory size555.5 KiB
2020-09-29T22:27:03.804466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33.45166667
Q361.21654793
95-th percentile162.2529989
Maximum11702.22
Range11702.22
Interquartile range (IQR)61.21654793

Descriptive statistics

Standard deviation130.4086734
Coefficient of variation (CV)2.575402309
Kurtosis3263.991441
Mean50.63623377
Median Absolute Deviation (MAD)33.33333333
Skewness43.60044406
Sum3178689.575
Variance17006.42209
MonotocityNot monotonic
2020-09-29T22:27:04.231246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01771524.9%
 
36580.1%
 
29.28570.1%
 
36.6530.1%
 
23.8490.1%
 
25.62480.1%
 
26.84470.1%
 
18.92450.1%
 
11.6450.1%
 
1.84450.1%
 
Other values (31901)4461362.7%
 
(Missing)832711.7%
 
ValueCountFrequency (%) 
01771524.9%
 
0.11999999732< 0.1%
 
0.18999999761< 0.1%
 
0.192< 0.1%
 
0.23999999461< 0.1%
 
ValueCountFrequency (%) 
11702.221< 0.1%
 
11529.449711< 0.1%
 
11515.531< 0.1%
 
58083< 0.1%
 
4919.041< 0.1%
 

consumo_total
Real number (ℝ≥0)

ZEROS

Distinct56015
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1695.847222
Minimum0
Maximum119726.94
Zeros2451
Zeros (%)3.4%
Memory size555.5 KiB
2020-09-29T22:27:04.619484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.49
Q1340.9525
median896.175
Q31808.9025
95-th percentile5564.1965
Maximum119726.94
Range119726.94
Interquartile range (IQR)1467.95

Descriptive statistics

Standard deviation3555.697457
Coefficient of variation (CV)2.096708601
Kurtosis195.8775277
Mean1695.847222
Median Absolute Deviation (MAD)664.505
Skewness10.99825971
Sum120578129.2
Variance12642984.41
MonotocityNot monotonic
2020-09-29T22:27:04.924754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024513.4%
 
3.05700.1%
 
1.22680.1%
 
3.66420.1%
 
6.71410.1%
 
1.83400.1%
 
7.93390.1%
 
4.88360.1%
 
6.1360.1%
 
9.76360.1%
 
Other values (56005)6824396.0%
 
ValueCountFrequency (%) 
024513.4%
 
0.013< 0.1%
 
0.053< 0.1%
 
0.125< 0.1%
 
0.2418< 0.1%
 
ValueCountFrequency (%) 
119726.941< 0.1%
 
117150.911< 0.1%
 
1010351< 0.1%
 
95117.771< 0.1%
 
94078.21< 0.1%
 

consumo_prom
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct62214
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.2173991
Minimum0
Maximum89691.77344
Zeros2451
Zeros (%)3.4%
Memory size555.5 KiB
2020-09-29T22:27:05.270195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.867208333
Q123.01013907
median31.69381809
Q345.48491686
95-th percentile188.219501
Maximum89691.77344
Range89691.77344
Interquartile range (IQR)22.47477779

Descriptive statistics

Standard deviation1069.949262
Coefficient of variation (CV)9.620340614
Kurtosis2599.541185
Mean111.2173991
Median Absolute Deviation (MAD)10.31349875
Skewness43.38268186
Sum7907779.51
Variance1144791.422
MonotocityNot monotonic
2020-09-29T22:27:05.730647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024513.4%
 
1.22620.1%
 
3.05550.1%
 
4.27430.1%
 
6.71390.1%
 
1.83380.1%
 
4.88380.1%
 
3.66380.1%
 
9.76370.1%
 
7.93360.1%
 
Other values (62204)6826596.0%
 
ValueCountFrequency (%) 
024513.4%
 
0.0099999997761< 0.1%
 
0.012< 0.1%
 
0.052< 0.1%
 
0.050000000751< 0.1%
 
ValueCountFrequency (%) 
89691.773441< 0.1%
 
87179.611< 0.1%
 
80555.011< 0.1%
 
56873.961< 0.1%
 
54935.991< 0.1%
 

consumo_prom_no_dom
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct37440
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.7601718
Minimum0
Maximum89691.77344
Zeros8109
Zeros (%)11.4%
Memory size555.5 KiB
2020-09-29T22:27:06.166975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.2754167
median19.28000034
Q354.186875
95-th percentile333.6616663
Maximum89691.77344
Range89691.77344
Interquartile range (IQR)47.9114583

Descriptive statistics

Standard deviation1095.817805
Coefficient of variation (CV)8.64481161
Kurtosis2364.161672
Mean126.7601718
Median Absolute Deviation (MAD)16.85000034
Skewness40.71654298
Sum9012901.734
Variance1200816.661
MonotocityNot monotonic
2020-09-29T22:27:06.487465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0810911.4%
 
1.223300.5%
 
1.832900.4%
 
3.052600.4%
 
4.272160.3%
 
7.932030.3%
 
3.662020.3%
 
4.882010.3%
 
6.11930.3%
 
6.711900.3%
 
Other values (37430)6090885.7%
 
ValueCountFrequency (%) 
0810911.4%
 
0.0099999997761< 0.1%
 
0.012< 0.1%
 
0.0121< 0.1%
 
0.014999999661< 0.1%
 
ValueCountFrequency (%) 
89691.773441< 0.1%
 
87179.611< 0.1%
 
80555.011< 0.1%
 
56873.961< 0.1%
 
54935.991< 0.1%
 

bimestre
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
2
23942 
3
23822 
1
23338 
ValueCountFrequency (%) 
22394233.7%
 
32382233.5%
 
12333832.8%
 
2020-09-29T22:27:06.829523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-29T22:27:07.027775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:27:07.246110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

consumo_total_no_dom
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct27336
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean436.0603092
Minimum0
Maximum119726.94
Zeros8109
Zeros (%)11.4%
Memory size555.5 KiB
2020-09-29T22:27:07.595841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.98
median54.055
Q3230.43
95-th percentile1695.6175
Maximum119726.94
Range119726.94
Interquartile range (IQR)219.45

Descriptive statistics

Standard deviation2126.152162
Coefficient of variation (CV)4.875821343
Kurtosis798.0749258
Mean436.0603092
Median Absolute Deviation (MAD)52.875
Skewness22.5073679
Sum31004760.1
Variance4520523.018
MonotocityNot monotonic
2020-09-29T22:27:08.130154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0810911.4%
 
1.224020.6%
 
1.833160.4%
 
3.053020.4%
 
7.932190.3%
 
1.182170.3%
 
4.882120.3%
 
4.272120.3%
 
3.662110.3%
 
6.11950.3%
 
Other values (27326)6070785.4%
 
ValueCountFrequency (%) 
0810911.4%
 
0.013< 0.1%
 
0.031< 0.1%
 
0.053< 0.1%
 
0.081< 0.1%
 
ValueCountFrequency (%) 
119726.941< 0.1%
 
117150.911< 0.1%
 
1010351< 0.1%
 
89691.81< 0.1%
 
88204.371< 0.1%
 

gid
Real number (ℝ≥0)

UNIQUE

Distinct71102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35551.5
Minimum1
Maximum71102
Zeros0
Zeros (%)0.0%
Memory size555.5 KiB
2020-09-29T22:27:08.468685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3556.05
Q117776.25
median35551.5
Q353326.75
95-th percentile67546.95
Maximum71102
Range71101
Interquartile range (IQR)35550.5

Descriptive statistics

Standard deviation20525.52376
Coefficient of variation (CV)0.5773462092
Kurtosis-1.2
Mean35551.5
Median Absolute Deviation (MAD)17775.5
Skewness-2.602398177e-17
Sum2527782753
Variance421297125.5
MonotocityNot monotonic
2020-09-29T22:27:08.677862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
68061< 0.1%
 
108961< 0.1%
 
88491< 0.1%
 
149941< 0.1%
 
129471< 0.1%
 
27081< 0.1%
 
6611< 0.1%
 
47591< 0.1%
 
211511< 0.1%
 
Other values (71092)71092> 99.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
711021< 0.1%
 
711011< 0.1%
 
711001< 0.1%
 
710991< 0.1%
 
710981< 0.1%
 

indice_des
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
bajo
29248 
popular
16539 
alto
15516 
medio
9799 
ValueCountFrequency (%) 
bajo2924841.1%
 
popular1653923.3%
 
alto1551621.8%
 
medio979913.8%
 
2020-09-29T22:27:09.193232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-29T22:27:09.421753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:27:09.558419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length4
Mean length4.835644567
Min length4

latitud
Real number (ℝ≥0)

Distinct22930
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.39227276
Minimum19.13586653
Maximum19.57910261
Zeros0
Zeros (%)0.0%
Memory size555.5 KiB
2020-09-29T22:27:09.773985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19.13586653
5-th percentile19.27217463
Q119.34407317
median19.39291026
Q319.44681849
95-th percentile19.49744601
Maximum19.57910261
Range0.4432360842
Interquartile range (IQR)0.1027453211

Descriptive statistics

Standard deviation0.07054946408
Coefficient of variation (CV)0.003638019377
Kurtosis-0.3299967947
Mean19.39227276
Median Absolute Deviation (MAD)0.05121505235
Skewness-0.2209675789
Sum1378829.378
Variance0.004977226881
MonotocityNot monotonic
2020-09-29T22:27:10.010180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19.3009469921< 0.1%
 
19.4954597821< 0.1%
 
19.5112685221< 0.1%
 
19.4488818321< 0.1%
 
19.5031486521< 0.1%
 
19.5143312121< 0.1%
 
19.5108167821< 0.1%
 
19.4966164613< 0.1%
 
19.4171689613< 0.1%
 
19.5116013612< 0.1%
 
Other values (22920)7091799.7%
 
ValueCountFrequency (%) 
19.135866533< 0.1%
 
19.136289973< 0.1%
 
19.169514452< 0.1%
 
19.17289733< 0.1%
 
19.1739933< 0.1%
 
ValueCountFrequency (%) 
19.579102613< 0.1%
 
19.575032333< 0.1%
 
19.574567263< 0.1%
 
19.57185673< 0.1%
 
19.571418773< 0.1%
 

longitud
Real number (ℝ)

Distinct22930
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-99.13289588
Minimum-99.33770342
Maximum-98.95046917
Zeros0
Zeros (%)0.0%
Memory size555.5 KiB
2020-09-29T22:27:10.493075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-99.33770342
5-th percentile-99.2236241
Q1-99.17248433
median-99.13519579
Q3-99.09663337
95-th percentile-99.02915715
Maximum-98.95046917
Range0.3872342535
Interquartile range (IQR)0.07585096428

Descriptive statistics

Standard deviation0.05789023819
Coefficient of variation (CV)-0.0005839659749
Kurtosis0.03317853179
Mean-99.13289588
Median Absolute Deviation (MAD)0.0378663909
Skewness0.1247230301
Sum-7048547.163
Variance0.003351279677
MonotocityNot monotonic
2020-09-29T22:27:10.724671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-99.0890349321< 0.1%
 
-99.2075157421< 0.1%
 
-99.1436926121< 0.1%
 
-99.1375628621< 0.1%
 
-99.2042146521< 0.1%
 
-99.1858947221< 0.1%
 
-99.1582172821< 0.1%
 
-99.1930681313< 0.1%
 
-99.1707142613< 0.1%
 
-99.1412796112< 0.1%
 
Other values (22920)7091799.7%
 
ValueCountFrequency (%) 
-99.337703423< 0.1%
 
-99.327994133< 0.1%
 
-99.325920983< 0.1%
 
-99.325443263< 0.1%
 
-99.325025133< 0.1%
 
ValueCountFrequency (%) 
-98.950469173< 0.1%
 
-98.951286673< 0.1%
 
-98.953346343< 0.1%
 
-98.954080293< 0.1%
 
-98.957691983< 0.1%
 

Interactions

2020-09-29T22:26:35.597181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:35.770916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:35.951900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:36.111364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:36.280023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:36.450766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:36.613460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:36.774750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:36.947215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:37.124064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:37.284139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:37.464129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:37.648053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:37.855104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:38.038013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:38.231350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:38.532573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:38.714740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:38.899381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:39.113512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:39.311458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:39.498159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:39.714548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:39.877273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:40.064337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:40.224164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:40.394726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:40.563017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:40.720736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:40.943226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:41.124314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:41.297189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:41.458537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:41.647566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:41.820963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:42.013519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:42.192954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:42.377354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:42.558400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:42.730536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:42.903651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:43.103411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:43.300015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:43.474316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:43.666026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:43.839033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:44.029336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:44.315904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:44.497946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:44.679356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:44.852219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:45.027478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:45.212038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:45.398804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:45.571074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:45.763284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:45.926423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:46.103194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:46.261745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:46.431841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:46.599439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:46.757999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:46.918212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:47.090134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:47.262450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:47.424432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:47.602675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:47.762254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:47.940686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:48.100817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:48.269358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:48.440841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:48.600260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:48.761863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:48.935484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:49.116544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:49.278006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:49.461283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:49.634799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:49.826777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:50.012228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:50.196302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:50.377310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:50.550387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:50.723686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:50.906516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:51.230477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:51.404469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:51.597918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:51.773734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:51.968856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:52.148509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:52.335224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:52.520933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:52.698069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:52.875186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:53.074680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:53.266480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:53.442749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:53.644289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:53.806984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:53.987064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:54.151545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:54.338910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:54.511489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:54.673328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:54.837283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:55.013617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:55.187995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:55.353708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:55.538174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:55.727937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:55.937966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:56.126940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:56.325463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:56.526580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:56.717662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:56.910813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:57.110445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:57.312867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:57.502698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-09-29T22:27:10.962604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-29T22:27:11.275832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-29T22:27:11.653846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-29T22:27:12.178429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-29T22:27:12.474291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-29T22:26:58.007650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:58.608058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:59.268821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T22:26:59.511580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

consumo_total_mixtoanionomgeoconsumo_prom_domconsumo_total_domalcaldiacoloniaconsumo_prom_mixtoconsumo_totalconsumo_promconsumo_prom_no_dombimestreconsumo_total_no_domgidindice_deslatitudlongitud
0159.722019Gustavo A. Madero42.566364468.23gustavo a. madero7 de noviembre53.24000631.0042.0666673.05000033.0557250alto19.455260-99.112662
10.002019Gustavo A. Madero35.936667107.81gustavo a. madero7 de noviembre0.00000115.1328.7825007.32000037.3257253medio19.455260-99.112662
20.002019Gustavo A. Madero24.586000122.93gustavo a. madero7 de noviembre0.00000197.9632.99333375.030000375.0357255popular19.455720-99.113582
30.002019Gustavo A. Madero0.0000000.00gustavo a. maderonueva tenochtitlan0.00000253.5384.51000084.5100003253.5357267bajo19.459647-99.104469
456.722019Azcapotzalco67.436250539.49azcapotzalcoprohogar56.72000839.3576.304545121.5700003243.1457330bajo19.474161-99.146750
5439.772019Azcapotzalco35.675769927.57azcapotzalcotrabajadores del hierro54.971251399.6737.82891910.776667332.3357273bajo19.478613-99.150571
6991.802019Azcapotzalco22.3818844633.05azcapotzalcobarrio coltongo123.975007693.6433.305801129.29937532068.7957275bajo19.480211-99.152316
70.002019Azcapotzalco0.0000000.00azcapotzalcobarrio coltongo0.00000305.00152.500000152.5000003305.0057276popular19.479096-99.148920
8184.862019Azcapotzalco33.6611761716.72azcapotzalcotrabajadores del hierro46.215001903.6633.9939292.08000032.0857277bajo19.478585-99.148847
910.982019Azcapotzalco51.912500207.65azcapotzalcotrabajadores del hierro10.98000237.5429.6925006.303333318.9157281bajo19.477273-99.147921

Last rows

consumo_total_mixtoanionomgeoconsumo_prom_domconsumo_total_domalcaldiacoloniaconsumo_prom_mixtoconsumo_totalconsumo_promconsumo_prom_no_dombimestreconsumo_total_no_domgidindice_deslatitudlongitud
71092148.442019Cuauhtémoc22.144688708.63cuauhtemocguerrero37.110000867.1023.43513510.030000110.03226bajo19.451196-99.144366
71093105.782019Cuauhtémoc23.407368889.48cuauhtemocguerrero26.4450011006.3723.40395411.110000111.11227bajo19.451146-99.144016
71094336.232019Cuauhtémoc24.44145911560.80cuauhtemocguerrero56.03833313188.2025.91005943.03933311291.18228bajo19.450613-99.142731
71095NaN2019Cuauhtémoc42.221111379.99cuauhtemocguerreroNaN379.9937.9990000.00000010.00229bajo19.449764-99.142259
71096794.272019Cuauhtémoc16.3670101751.27cuauhtemocguerrero397.1349872563.6323.0957669.045000118.09232bajo19.448385-99.139017
71097NaN2019Cuauhtémoc20.0531123930.41cuauhtemocguerreroNaN4286.2819.30756813.6873081355.87233bajo19.448564-99.139940
7109871.302019Cuauhtémoc21.1266159549.24cuauhtemocguerrero35.6500019796.1220.97670213.5069231175.59238popular19.449339-99.145719
71099759.162019Cuauhtémoc27.5277784707.25cuauhtemocguerrero94.8949995692.8129.34438115.0933341226.40239bajo19.448392-99.145930
71100402.652019Cuauhtémoc30.605000550.89cuauhtemocguerrero100.662498963.1541.8760879.61000019.61244bajo19.447587-99.142509
7110141.202019Cuauhtémoc22.5077108552.94cuauhtemocguerrero13.7333339000.0721.95136615.0344441405.93247bajo19.447402-99.139725